{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27f1607b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27f1607b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)    # 将每一个维度的特征求平均\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.006172\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -20.105584 analytic: -20.105584, relative error: 9.552025e-12\n",
      "numerical: -7.368607 analytic: -7.368607, relative error: 4.383453e-12\n",
      "numerical: -11.924388 analytic: -11.924388, relative error: 3.725044e-11\n",
      "numerical: -2.865181 analytic: -2.865181, relative error: 1.715432e-11\n",
      "numerical: -13.968588 analytic: -13.968588, relative error: 6.862809e-12\n",
      "numerical: -4.704444 analytic: -4.704444, relative error: 1.450526e-11\n",
      "numerical: 29.307979 analytic: 29.307979, relative error: 4.124087e-12\n",
      "numerical: -0.477164 analytic: -0.477164, relative error: 4.779537e-11\n",
      "numerical: -0.617253 analytic: -0.617253, relative error: 6.136149e-11\n",
      "numerical: 10.591882 analytic: 10.591882, relative error: 1.290624e-11\n",
      "numerical: -2.502230 analytic: -2.502230, relative error: 1.467889e-10\n",
      "numerical: 3.464440 analytic: 3.464440, relative error: 4.736896e-11\n",
      "numerical: -9.264989 analytic: -9.264989, relative error: 3.445983e-11\n",
      "numerical: 17.648359 analytic: 17.648359, relative error: 3.019584e-16\n",
      "numerical: -4.420195 analytic: -4.420195, relative error: 1.714169e-11\n",
      "numerical: 11.104781 analytic: 11.104781, relative error: 3.560990e-11\n",
      "numerical: -49.276887 analytic: -49.276887, relative error: 4.864886e-12\n",
      "numerical: 26.962888 analytic: 26.962888, relative error: 9.664823e-14\n",
      "numerical: 9.730881 analytic: 9.730881, relative error: 3.010406e-12\n",
      "numerical: 8.051844 analytic: 8.051844, relative error: 9.072005e-11\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** \n",
    "\n",
    "(1) 在一些不可微的点处可能会出现梯度不匹配的情况；例如ReLU激活函数 $f(x)=max(0,x)$ 在0处是不可导的\n",
    "\n",
    "(2) 使安全间隔相对较大，来保证在较大的数据差异下才会有梯度？？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.006172e+00 computed in 0.145387s\n",
      "Vectorized loss: 9.006172e+00 computed in 0.010027s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.113301s\n",
      "Vectorized loss and gradient: computed in 0.005015s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 409.377699\n",
      "iteration 100 / 1500: loss 241.939934\n",
      "iteration 200 / 1500: loss 146.579301\n",
      "iteration 300 / 1500: loss 91.114121\n",
      "iteration 400 / 1500: loss 56.630994\n",
      "iteration 500 / 1500: loss 35.725232\n",
      "iteration 600 / 1500: loss 23.970235\n",
      "iteration 700 / 1500: loss 15.983305\n",
      "iteration 800 / 1500: loss 11.635656\n",
      "iteration 900 / 1500: loss 9.670186\n",
      "iteration 1000 / 1500: loss 7.587809\n",
      "iteration 1100 / 1500: loss 6.346114\n",
      "iteration 1200 / 1500: loss 5.961449\n",
      "iteration 1300 / 1500: loss 5.120075\n",
      "iteration 1400 / 1500: loss 5.052786\n",
      "That took 7.554116s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27f16076160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.383837\n",
      "validation accuracy: 0.385000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\David\\Desktop\\CS231n-code\\assignment1\\cs231n\\classifiers\\linear_svm.py:86: RuntimeWarning: overflow encountered in double_scalars\n",
      "  \n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:32: RuntimeWarning: overflow encountered in reduce\n",
      "  return umr_sum(a, axis, dtype, out, keepdims)\n",
      "C:\\Users\\David\\Desktop\\CS231n-code\\assignment1\\cs231n\\classifiers\\linear_svm.py:86: RuntimeWarning: overflow encountered in multiply\n",
      "  \n",
      "C:\\Users\\David\\Desktop\\CS231n-code\\assignment1\\cs231n\\classifiers\\linear_svm.py:81: RuntimeWarning: overflow encountered in subtract\n",
      "  sub_result = scores - scores[range(y.shape[0]), y].reshape(-1, 1) + 1.0   # get NxC\n",
      "C:\\Users\\David\\Desktop\\CS231n-code\\assignment1\\cs231n\\classifiers\\linear_svm.py:81: RuntimeWarning: invalid value encountered in subtract\n",
      "  sub_result = scores - scores[range(y.shape[0]), y].reshape(-1, 1) + 1.0   # get NxC\n",
      "C:\\Users\\David\\Desktop\\CS231n-code\\assignment1\\cs231n\\classifiers\\linear_svm.py:83: RuntimeWarning: invalid value encountered in maximum\n",
      "  \n",
      "C:\\Users\\David\\Desktop\\CS231n-code\\assignment1\\cs231n\\classifiers\\linear_svm.py:111: RuntimeWarning: invalid value encountered in greater\n",
      "  dW = np.dot(X.T, margin)             # DxC\n",
      "C:\\Users\\David\\Desktop\\CS231n-code\\assignment1\\cs231n\\classifiers\\linear_svm.py:117: RuntimeWarning: overflow encountered in multiply\n",
      "  # row_sum = np.sum(margin, axis=1)                  # 1 by N\n",
      "C:\\Users\\David\\Desktop\\CS231n-code\\assignment1\\cs231n\\classifiers\\linear_classifier.py:70: RuntimeWarning: invalid value encountered in add\n",
      "  self.W += - learning_rate*grad\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.381980 val accuracy: 0.403000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.369347 val accuracy: 0.377000\n",
      "lr 5.000000e-05 reg 2.500000e+04 train accuracy: 0.135531 val accuracy: 0.129000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.403000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "for lr in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        svm = LinearSVM()      # 创建SVM分类器\n",
    "        svm.train(X_train, y_train, learning_rate=lr, reg=reg,\n",
    "                  num_iters=3000, verbose=False)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        training_accuracy = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        validation_accuracy = np.mean(y_val == y_val_pred)\n",
    "        results[(lr, reg)] = (training_accuracy, validation_accuracy)\n",
    "#         print(training_accuracy, validation_accuracy)\n",
    "        if validation_accuracy > best_val:\n",
    "            best_val = validation_accuracy\n",
    "            best_svm = svm\n",
    "                                      \n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1e-07, 25000.0)\n",
      "(1e-07, 50000.0)\n",
      "(5e-05, 25000.0)\n",
      "(5e-05, 50000.0)\n"
     ]
    }
   ],
   "source": [
    "for x in results:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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pAVQnGvYBwOU5JM+zkp7K57u31sWKFECzbP++mTsIIYTQGZosgKpFw35fZSJJR5OiIAwBPlx27H0Vx64QSbtckXapOySdI2kXSe8uLQWOCyGE0EZVRsG1Mhp2oWPLFakBlUq/8giqZlmpF0IIoQP1ZTTsHhzbuACyvUejNCGEEDpTk01wS6NhAy+RBhUcVp5A0ha2n8yrS6Nhk6JmXyrpXNIghC2ASfUuVmQU3DrAyaRIpwB3AafanlXodkIIIbRFs4MQbC+SVIqGPRC4qBQNG5hs+zpSNOyPAAuBmeRo2DndlaQBC4uAo+uNgINiTXAXAY8An8jrnwL+G/hYkRvKw/omAy/ZXiEgkqRPkIZ5G5hq+7DKNCGEEHqmr6Jh531nAGcUvVaRAmhz2weVrX8nzwNR1HGkeSVWmJo1T2x0ErCb7ZmSNmzivCGEEOooDULoVEVGwXVL+kBpJb+Y2l3k5JJGk9oIL6yR5F+Bn9ieCWD79SLnDSGE0NiqEIrni8D/5L4gATOAIwue/4ekWVSH19i/JYCkP5HaG0+xfVPBc4cQQqijJxPS9aUio+AeAnaQNCKvz25wCACS9gdet/2gpAl1rr8FMIE0ZO+Pkra1/feKcx0FHAUwZsyYIpcPIYRAZ09IV7MAknS47UskHV+xHQDb5zY4927AREn7AsOAEZIusX14WZoXgftsLySFbniCVCA9UH4i2xcAFwCMHz++2DSnIYSwmuv0GlC9PqC18s/hVZa1G53Y9km2R9seSxpLfntF4QNwLbAHgKRRpCa5Z5q5gRBCCLU12wckaW9JT0h6StKJVfYfL+kxSQ9L+j9Jm5btWyzpobxc1+haNWtAts/Pv95m+08VGdit0J1UUTGe/GZgT0mPkQKdftX2mz09dwghhGWaHQVXMBr2n4HxtrskfRE4Gzgk7+u2vWPR6xUZBfefBbfVZPvO0jtAtr+dCx+cHG97a9vb2b68mfOGEEKorQej4JZGw7a9gBRq54CKc95huyuv3kfqv++Ren1AuwC7AhtU9AONII1YCyGE0OFaEQ27zGeB8tkShkmaTIqEcJbta+tdrN4ouCGkvp5BLD+MejZwcL2ThhBCaL8qgxBG5QKi5II8yKukcERrSYeTglR/qGzzGNsvSxoH3C5pmu2na+WvXh/QXcBdki62/bda6UIIIXSmVkXDzrHgvgF8KE9AV7rey/nnM5LuBHYCmi+AynRJOoc0zeqwsgvFdAwhhNDhmgzFUyQa9k7A+cDe5dFrJK0HdNmen0c170YaoFBTkUEIvwIeBzYDvgM8R8V7OiGEEDpPs4MQbC8CStGw/wJcWYqGLWliTnYOqXvmqorh1lsBkyVNBe4g9QE9Rh1FakAjbf+XpOPKmuXuKnBcCCGENurJi6gFomF/pMZx9wDbNXOtIgXQwvzzFUn7kdoDezzsLoQQQt/p5EgIRQqg03Mg0n8nvf8zAvhyS3MVQghhpXV6KJ4iBdDUPPvpLJaFzdmopbkKIYTQKzq5ACoyCOFZSZdJWrNs2401U4cQQugIpVA8paXTFCmApgF/JE2VsHneVu1lpRBCCB1kVZiQzrZ/mofWXS/pa9R4MzaEEEJn6cSCp6RIDUgAOSL2PwBfBd7VykyFEEJYeT2pAa3kdAxHSHoyL0c0ulaRAmjfspt5BfgwsHehOwkhhNA2zRZAZdMx7ANsDXxS0tYVyUrTMWwPXE2OdiBpfeBkUvDS9wIn5+gINTWcETVnoFqSPzS8mxBCCG3VZBPc0ukYACSVpmNYGtHA9h1l6e8DShON7gXcantGPvZWUmXlsloXq9cHVD4jagghhH6myoR0jaJhr8x0DNWOfUe9/NWdETVXx2bb/kG9k4QQQug8PYiGvTLTMRQ+tqRuH5DtxcDEemlCCCF0riYHITQ7HcPEsukYCh1brsgghHsk/VjSByW9u7QUOC6EEEIb9WAU3NLpGCQNIU3HcF15grLpGCaWT8dAiqC9p6T18uCDPfO2moq8B7Rr/nlq2TaTRsOFEELoUM3GgrO9SFJpOoaBwEWl6RiAybavY/npGACetz3R9gxJp7Fsup5TSwMSamlYANneo3DuQwghdJRmQ/D0dDqGvO8i4KKi1ypSAyJPw1A5I+qptY8IIYTQbv0+GraknwNrkiJhXwgcDExqcb5CCCGspE4vgIoMQtjV9r8AM21/B9iF5Uc6hBBC6FCdHIy0SAHUnX92SXo7aYbUzVqXpRBC6D+WLFnCbbfdxsc//nG22morttpqKw466CBuueWWtn/orwrRsG+QtC5p5MMU0gi4C4teIL/MOhl4yfb+NdIcDFwFvMf25GppQgih0yxcuJDPf/7z3H///XR1dS3dPmXKFL785S/z7ne/mwsvvJChQ4e2LY+dWPCUNKwB2T7N9t9t/xrYFHiX7W81cY3jgL/U2ilpOPAl4P4mzhlCCG33ne98h3vvvXe5wqekq6uLyZMn841vfKMNOUt6MiFdgWjYu0uaImlRrjyU71ss6aG8XFd5bKWaBZCkj1UuwH7AP+Tfi9zI6HxMvRrTaaRoqvOKnDOEEDrBrFmzuPrqq5k3r/ZH17x587j++uuZPn16H+ZsmRZFw34eOBK4tMopum3vmJeGUXTqNcH9Y519Bn7T6OTAD4ETqBHQNL9Ru4ntGyR9pcD5QgihI9x8880MHDiwYboBAwbw+9//nk996lN9kKsVtSAa9nN535KVzVu9YKSfXpkTS9ofeN32g5ImVNk/APgBqSRtdK6jgKMAxowZszLZCiGEXvHmm28yf/78hunmzZvX9hpQE5qNhl1pWI62vQg4y/a19RIXeQ/o29W2F3gRdTdgoqR9SS+wjpB0ie3S3BHDgW2BO3M4h42A6yRNrByIkMOFXwAwfvz4mA48hNB266yzDkOGDKG7u7tuuqFDh7Luuuv2Ua6WV6UAajQdQ9MRrSuMsf2ypHHA7ZKm2X66VuIiw7Dnli2LSW2DYxsdZPsk26NtjyUFtLu9rPDB9izbo2yPzWnuIwW3i1FwIYSOt+eee7JkSbFWqL33bt8k0hWDEKbbHl+2XFCRvOmI1uVsv5x/PgPcCexUL32RUXDfL1vOACbQYJKheiSdKimmeAgh9GujRo1ir732qjvEeujQoXzoQx9i44037sOcLdOKaNi15CjYQ/Pvo0itYI/VO6ZQLLgKawLjmjnA9p2k0nC5oHYVaSb0IC8hhNA2Z511Fi+88AKPP/74Ck1xa6yxBptvvjnnnntum3LXmmjYkt4DXAOsB/yjpO/Y3gbYCjg/D04YQOoDWrkCSNI0lrUBDgQ2YPmpGUIIYbW0xhprcPnll3PNNddw/vnn89xzzwGwySab8PnPf56DDjqorS+hQvMvohaIhv0AqWmu8rh7gO2auVaRGlB59IJFwGu2m4vvHUIIq6ghQ4ZwyCGHcMghh7Bo0SJsM3jw4HZnC+j8YKRFCqC3KtZHSHrL9sJWZCiEEPqrQYN60qvRWv29AJpCGhUxkzREb13gFUmvA/9q+8EW5i+EEEIPlULxdKoiw7BvAvbNQ6ZHkoZhXwn8G/DTVmYuhBBCz3V6NOwiBdB42zeXVmzfAuxu+z6gvb1rIYQQ6urkAqhIE9wMSV8DLs/rhwAzc9C6lY4FFEIIoTU6fRBCkRrQYaQhd9fmZZO8bSDwidZlLYQQwsro9Ca4hjUg29OBYyWtbXtOxe6nWpOtEEIIvaETC56ShjUgSbtKeowcUkHSDpJi8EEIIXS4nkxI15dk1w90Kul+4GDgOts75W2P2N62D/JXLT9vAH9biVOMAtoTG7014n46W9xPZ+vE+9nU9ga9cSJJN5HusWS67fZFRq1Q6K0p2y/kKRNK2lanW9kHI2my7fG9lZ92i/vpbHE/nW1Vu59KnVTYVFOkAHpB0q6Ac3TULwF/aW22QgghrOqKjIL7AnA0aQqGF4Ed83oIIYTQY3VrQPldn0/Z/uc+yk9fqJyAqb+L++lscT+dbVW7n36lyCCEO2OunhBCCL2tSAF0BrAOcAVpWm4AbE9pbdZCCCGsyor0Ae0KbEOahO77efmPVmaqN0m6QtJDeXlO0kM10u0t6QlJT0k6sa/z2QxJx+a8Pirp7BppnpM0Ld/35L7OYzMK3k+/eD6STpH0Utm/uX1rpOsXz6eJ++kXz6dE0lckOU8dXW3/4rJ7LjQldWhekUgIe/RFRlrF9iGl3yV9H5hVmSb3df0E+ChpoMUDkq5rNJ1sO0jaAzgA2N72fEkb1km+R45k0bGK3E9/ej7ZD2wX+ZLW8c8nq3s//e35SNqElNfn6yTrtr1jH2VptVWkBrRKUHqR6RPAZVV2vxd4yvYztheQAq8e0Jf5a8IXSXOtzwew/Xqb87OyitxPf3o+q6P+9nx+AJwA1O9/CC232hRAwAdJ04k/WWXfO4AXytZfzNs60ZbAByXdL+kuSe+pkc7ALZIelHRUH+avWUXupz89H4BjJD0s6SJJ69VI01+eDzS+n37zfCRNBF6yPbVB0mGSJku6T9KBfZG31VHnzR/bA5JuAzaqsusbtn+bf/8k1Ws/kGZ6rdS2b0f17of0zNYD3g+8B7hS0jivOJpkN9sv5yatWyU9bvsPLc14Db1wP/3p+fwMOI2Uv9NIfaafqZK2vzyfIvfTn57P14E9C5xmTH4+44DbJU2z/XRv5jMUKIAkfazK5lnAtE5p/rH9kXr7JQ0CPgbsXCPJi6RpJkpGAy/3Tu6aV+9+JH0R+E3+gJ4kaQkp1tMbFed4Of98XdI1pGaStnzA9cL99JvnU07SL4AbapyjXzyfcnXup188H0nbAZsBU3NosdHAFEnvtf1qxTlKz+cZSXcCOwFRAPWyIk1wnwUuBP45L78Ajgf+JOlTLcxbb/oI8LjtF2vsfwDYQtJmOdzQoUCnjny5FvgwgKQtgSFUBFOUtJak4aXfSd/4HunjfBbV8H7oR89H0sZlq/9Elb97f3o+Re6HfvJ8bE+zvaHtsbbHkgrOd1cWPpLWkzQ0/z4K2I08G0DoXUUKoCXAVrYPsn0QsDUwH3gf8LVWZq4XHUpF85ukt0u6EcD2IuAY4GZSnLsrbT/a57ks5iJgnKRHSJ29R9h2+f0AbwPuljQVmAT8zvZNbcpvIw3vp589n7Pz8OqHgT2AL8Py/97oX8+n4f30s+dTlaTxki7Mq1sBk/PzuYM0SCYKoBYo8iLqNNvbla2L1Py2raQ/l6ZoCCGEEJpRZBDCHyXdAFyV1w8G/pCbDv7espyFEEJYpRWpAYnUgf8B0miXu4FfVxl1FUIIIRRWJBKCJd0NLCANrZwUhU8IIYSV1XAQgqRPkDpKDyZFErhf0sGtzlgIIYRVW5EmuKnAR0vv/EjaALjN9g59kL8QQgirqCLDsAdUvHD6ZsHjQmhI0pxeOs/FfVEzl3RPq69Rcb11Jf1bX14zhL5SpCC5SdLNko6UdCTwO+DGBseE0C/lqBk12d61j6+5LhAFUFglFRmE8FVJB5HeBhZwge1rWp6zsFrJoy3PBvYhDXY53fYVkgYAPwY+BDxL+tJ0ke2r65xrZ+BcYG1SVIUjbb8i6V+Bo0jRFp4iTTffJeliYAYp3MoUSW8BY4Bx+ecPbf8on3uO7bUlTQBOyeffFngQODwP2tk3X386MAUYZ3v/ijweCewHDAPWykEyf0uKizcY+GaOY3gWsLnSPFa35v+PXyX1xw4FrrF9cnN/7RA6hO1YYmnbAszJPw8CbgUGkiIFPA9sTBr8ciOp4NkImAkcXOU8F+e0g4F7gA3y9kNIBRbAyLL0pwPHlh17AzAwr5+SzzGUFJfuTWBwRX4nkGIijs55u5f0qsIwUmTozXK6y4AbquT3SFIomPXz+iBgRP59FKmAFDAWeKTsuD2BC/K+ATnfu7f7OcYSS0+WmjWg/C2w2ggFpXLLI2odG0IPfAC4zPZi4DVJd5GiY38AuMr2EuBVSXc0OM//I9VIbs0BJwcCr+R920o6ndSstTYpdEzJVfnaJb9zmqNovqTXSYViZSzBSc7xBXMNZSwwB3jG9rM5zWWkWlc1t9qekX8X8F1Ju5PCX70jX7PSnnn5c15fG9iCNgUyDWFl1CyAbA/vy4yE1V61kP71ttc7z6MHCpW4AAAgAElEQVS2d6my72LgQNtTcxPYhLJ9cyvSzi/7fTHV/69US9NMfsuv+c/ABsDOthdKeo5Um6ok4Ezb5zdxnRA6UoxmC53iD8Ahkgbmof67k94/uxs4SNIASW9j+UKjmieADSTtAiBpsKRt8r7hwCuSBpM+8FvhcVJw1bF5/ZDaSZezDvB6Lnz2ADbN298i5bvkZuAzktYGkPQO1Z+WPYSOtUpMSBdWCdcAuwBTSU2/J9h+VdKvgX8gTQPwV+B+Ut9LVbYX5OHYP5K0Dunf+A+BR4Fv5eP/Bkxj+Q/2XmG7Ow+bvknSdFIhWsSvgOslTQYeIhVk2H5T0p9ytPDfOw1C2Aq4NzcxzgEOBzpibq4QmtHwRdQQ2k3S2rbnSBpJ+kDfzRVzuHSSsvwK+AnwpO0ftDtfIXSaqAGF/uAGSeuShk+f1smFT/avko4g5ffPQPTXhFBFoRqQpF1JI3yWFli2/7d12QohhLCqa1gDkvRLYHNSu3RpmKqBKIBCCCH0WJFgpH8BtnZ0FoUQQuhFRYZhP0J6Az2EEELoNfUiIVxPamobDjwmaRJlL97Zntj67IUQQlhV1esD+o8+y0UIIYTVTpE+oO/Z/lqjbSGEEEIzivQBfbTKtn16OyMhhBBWL/X6gL5ImghrnKSHy3YNB/7U6oyFEEJYtdWrAV0K/CNwXf5ZWna2fXgf5C2EXiPp7hwBG0lHSPp9kbQ9uM643ppmPIRVXc0CyPYs288BR5Mi8pYWcjTh0EEkHSZpsqQ5kl6R9HtJH8j7TpF0SVlaS5qb086R9PeKc70zp/lRxfZBFce+KOmcPGtprXy9Q9L1OU+WNLpi/zBJF0uandMc1zt/kdps/4/tXmlGzn+DCWXnfsb22r1x7hBWdUX6gKYAb5AiET+Zf39W0pQ89XFoM0nHkyI+f5c0idkY4KfAAXUO28H22nlZt2LfEaQpqj9Z48vGNvlD9sPAp3L6WpaQZjQ9uMb+00hhnsaQ+hu/Lukjdc4X2kBSxI0Mva5IAXQTsK/tUbZHkgYgXEnqH/ppKzMXGstTDpwKHG37N7bn2l5o+3rbX+3B+UQqVE4iTX62X620tv9Kmrp6xzppXrH9M+DBGkn+BTjV9t9tPwJcRJquujJfa+Ra0rvKtm0kqVvSyLzcKOkNSTNzresdNe7xc5LuLFvfW9ITkmZJOo+ySeUkbSHpDklvSpou6Zf5b46ky4C3A7/PNcLjS7XHsuNHS7pB0gxJT0r6TNm+0yVdJukSSW9JekTSu2v9LSX9ONe4Zkt6IMdoLO0bJOlbkp7O+ydLenvet52k23IeXpV0Qt5+iaRTys7xEaWJ8ErrL0r6qqRpQFfe9k1Jz+T8PippufcBJX1e0uNl97ODpJMkXVGR7meS4lWP1VyRAmi87aVTF9u+hTQH/X3A0JblLBS1C2nmzGt66XwTSLWoK4CrSAVEVUrz0uwGPNWTCylNPLchaQ6gkqnANpVpbXcD1wKfLNt8CPB/tt8k/Vv+BakmtSmwEDivQB42BK4GTgRGkabdfl95EuB0YGNga2AcaV4hbH8SeBnYJ9ckz61yiSuAZ0kF1SHA2ZI+VLb/QOCXpGnCfw/8aIUzLHM/sD2wfs7zVZJK/we/Sqpl7p3P9TlgXi4sbwOuz/ewJXBnvb9JhUNJXzrXyet/JT3zdYAzgEuVJgpE0ieBb5Im+xsBfIxUk/4lsJ+kETndEODjeXtYjRUpgGZI+pqkTfNyAjBT0kBS80por5HAdNuLmjxuiqS/56X8Q+8I4He2Z5EGouynNA9PuYclzQUeA26l59MNlPpKyieYm0XtieIuZfkC6LC8Ddtv2L7Gdrft2aTmyA9VOUel/YGH8rELge+TmpnJ5/2r7f+zvcD268APCp4XSZsB7wVOtD3P9hTgv0k1zJK7bN9sezHpA7lebfKXtmfkZ3026UP+nXn354Cv237S9hLbD9meAUwEXrB9nu35tmfbLjpJHsB5tl/MXwCwfWWu1S6xfSnwHDC+LA9n2X7QyV9tv2D7ReBe4KCcbl/gZdtTCau1IgXQYcBo0rfP35K+YR4GDAQ+0bqshYLeBEb1oI3+3bbXzcuXACStRfqQ+FVOczfwCst/6EP6Fj6c9O9gF2DNfPwELRvYUOTDpTRabETZthHkwS5V3AasK2lnSZuTakq/LeVd0oWSnpc0G7idVKNp5O3AC6UV20tItSDyeTeSdKWkl/J5Ly543tK5p9ueW7btb0B502D53EZdwFq1TibphNy8NQuYmdOW8rIJ8HSVwzahhzXU7IXyFUlHSppa+vICvKtAHgD+hzRzK/ln1H5C4wLI9nTbx9reyfaOto/J3zYX2F6Zf9ihd9wLzCM15aysg0i1kgskvUoqfDaiSjNc/gZ8GTCZ1OyC7TvLBjbs0Ohitt8g1TbK0+5Amj67WvpFpGbBT5IKv9+WfbifAGwGvNf2CNIAiSJeIX1wAqA0oq98pN73SDEQt8vnPZKyPiJSvMRaXiZ9OSgvVMYALxXM21KS9gCOJz2jdYH1SAV4KS8vkKZNqVRrO8Bc8peHrFrQ4fL+rHHAz4AvAiPz4JXHC+QB4DfAzpK2ITXpXVojXViNNCyAJG0p6QJJt0i6vbT0ReZCY7mp7NvATyQdKGlNSYMl7SPp7CZPdwSpH2U7UlPQjsDuwPjc31PNmcAXcn9OVZKGsay/cGhZvwWkeaW+JWldSVsDnyHVMmq5lNSXsrT5LRtOqkHMzE2G365zjnI3ADtKOiDXIr8MlN/LcNIH9SxJmwBfqTj+NVK/0ApsP0sqoL8raaikHYFPs6yG2YzhwCJgOjAYOIXla0sXAqdL2lzJjpLWJ73HN0bSMZKGSBoh6b35mIdITazrSdoY+FKDPKxNKpDeII1X+RypBlSehxMk7ZTzsEX+m2G7i9RPeRnwJ9tNF8Jh1VOkCe4q0rTC3yR1dJaW0CFy5/fxpGf0Bumb6DGkZtNCJI0hDUD4oe1Xy5ZJpKavqkOtbT9EqoVVfjCXzjsI6AZK7xo9RfpAL/lWzu8LpGazM23fVier95A+iDcAbinbfi6pY/zNnKbmi6YV+X+NVKCdk48dQ+rsLzmZ1I8zi/Rh/uuKU3wX+E5ukvr/qlziEGALUlPb1aR+mjuK5K3CjaTn8CSp32U2qfZWcg7pef9f3ncBMCx/Qfkoqeb0OmkQQakP62LgL6RmwZuAy+tlwPbDpEESk/K130XZ3yrXiL9HGngxm1TrWa/sFP9D+nITzW8BKBaM9EHb8b5PCGGl5Ca8h4GNbEe0iFCoADqF9M3pGpafD2hGS3MWQlhl5L61HwFDbB/V7vyEzlCkAHq2ymbbrtruHUII5fK7SC+Rmg73iv6fUFJkFNxmVZYofEIIhTjFlVzb9rZR+HQ+LYsM8pSkE+ukO1gpvuP4sm0n5eOekLRXo2sVGQW3plL4jQvy+haS9i96MyGEEPqHHGDgJ6Sh8luT4kFuXSXdcNKoyfvLtm1NipyxDSkix0/z+WoqMgruv4EFQCnu1Iuk0CQhhBBWLe8FnnKK6r6ANDKyWlDj00jROOaVbTsAuDxH3HiWNOL1vVWOXarI2/Ob2z4kx3nCdrckNTqoVUaNGuWxY8e26/IhhNBSDz744HTbNd+ra8bee+/t6dOnl5/7Ztt71znkHSwf/aIyNiKSdgI2sX2DpK9UHHtfxbFVAwKXFCmAFkhag/xGdA6BMr/+Ia0zduxYJk+e3K7LhxBCS0n6W2+da/r06UyatCz038CBA98lqfwD9ALbF5RfvsppyqNhDCDFQzyySrq6x1ZTpAA6mfSS2iaSfkWKhFvt4iGEEDqIbRYvXly+abrt8bXSk2otm5StjyaFlCoZDmwL3JkbwjYCrlOalqPRsSuoWwDlprbHSWHV308q4Y6zPb3ecSGEEDpDRQHUyAPAFjmS+0ukQQWHlXbmyBpLg/Eqzav1FduTJXWTpuc4lxSIdwtS1Iya6hZAti3p2hwJ4XfN3EUIIYT2qlIDapR+kaRjgJtJMx5cZPtRSacCk21fV+fYRyVdSZqmZRFpksy6Fy/SBHefpPfYfqDwXYQQQmg72yxa1NxUYbZvJMUeLN9WNbiv7QkV62eQJiospEgBtAfw+dwxNpfUDGfb2xe9SAghhL7XbA2orxUpgPZpeS5CCCG0RCcXQEVeRD3d9t/KF+JF1BBC6HilGlBp6TRFCqBtyldyaIV+Mz2Dbbq7u5kxYwbTp0/nzTffZO7cuSxZsqTdWQshrAJsc8899/Cxj32MjTbaiI022ogDDjiAP/7xjzQK9twX+mUBlIPKvQVsL2l2Xt4iTc3w2z7L4UpYvHgxb775JnPmzGHx4sXYZsmSJXR1dfHmm2+yYMGCdmcxhNCPLVmyhE9/+tN89KMf5dprr+W1117jtdde4/rrr2efffbh0EMPbXoQQG/qSQ2oUTBSSV+QNE3SQ5LuLsWKkzRWUnfe/pCknze6Vs0CyPaZtocD59gekZfhtkfaPqnQnbSRbWbOnFn3G8isWbPa+o8jhNC/nXTSSVx11VV0dXUt91ljm7lz53LDDTdw/PHHty1/pVFwpaWRgsFIL7W9ne0dSfHgzi3b97TtHfPyhUbXK9IEd4OktXLmDpd0rqRNCxzXVvPmzStU/Z07d27DNCGEUOmtt97iP//zP+nq6qqZpquri1/84hfMnDmzD3O2vCZrQA2DkdqeXba6Fg3C7dRTpAD6GdAlaQfgBNL88f/b0wv2le7u7kLpFixY0BHttCGE/uWaa65h4MC6sw0AMGDAAK666qo+yNGKetAEVy0Y6QoBRSUdLelpUg3oS2W7NpP0Z0l3Sfpgo4sVKYAWOX1CHwCcZ/s8UjygjtbMIIMYkBBCaNbLL79c6ItuV1cXL79cNyRay1QpgEZJmly2VE6PXiigqO2f2N4c+Brwzbz5FWCM7Z2A40lheUbUy1+R94DeknQScDiwe24jHFzguLaSVLhmM2BAkXI4hBCWGTFiBEOGDGlYCA0dOpQRI+p+DrdULwcjrXQ5qZUM2/PJMyXYfjDXkLYEak5fUOST95B80s/afpVUHTunwHFtNWzYsELpBg0aRBunNwoh9FMTJ04s1HoiiQMPPLAPcrSiZgchUBaMVNIQUjDS5eK/SdqibHU/4Mm8fYPSDKiSxpGCkT5T72INCyDbr9o+1/Yf8/rztju+D6hoAbTWWmu1OCchhFXR6NGj2XPPPRk6dGjNNEOGDOGDH/wg48aN68OcLdNsH5DtRUApGOlfgCtLwUjzlAsAx0h6VNJDpKa2I/L23YGHJU0Frga+YHtGvesVaYLrlwYOHMiIESOYPXt2zTRrrrkmQ4YM6cNchRBWJb/85S/ZddddeeaZZ5g3b95y+4YNG8Ymm2zC5Zdf3qbcJc2+gNooGKnt42oc92vg181ca5Xu/Bg6dCjrrbfeCoXMoEGDWGeddaL2E0JYKeussw6TJk3i5JNPZsMNN2To0KEMGzaMUaNG8Y1vfIMHH3yQ9ddfv2356/RQPKtsDaikVNiUoiBIikEHIYRes9Zaa3HiiSdywgknMH16mqtz1KhRHfM504kFT0nDAkjSbsApwKY5fWk6hvY0avaQpEJj9kMIoScGDBjAhhtu2O5sLGdVmI7hv4AvAw8CnXsnIYQQltOTCen6UpE64izbv7f9uu03S0vLcxZCCGGl9VUw0rzvpHzcE5L2anStIjWgOySdA/yG/JIRgO0phe4mhBBCWzTbBFcWjPSjpJdSH5B0ne3HypJdavvnOf1EUjDSvXNBdChpCp+3A7dJ2tJ2zQwUKYDel3+Wvz1r4MMF7ymEEEIb9KAPaGkwUgBJpWCkSwugOsFIDwAuzxERnpX0VD7fvbUu1rAAsr1HM7kPIYTQOZosgKoFI31fZSJJR5NeQh3CssrIO4D7Ko5dIZBpuYZ9QJLWyVMwlILXfV/SOo2OCyGE0F59HIy00LHligxCuAh4C/hEXmYD/13guJQjaWAOz31Djf2fkPRYDu1wadHzhhBCqK9KLLjptseXLRdUHNKTYKSlQHfNHluoD2hz2weVrX8nxwAq6jhSTKEVwsHmoHYnAbvZnimpswbRhxBCP9dkE9zSYKTAS6RBBYeVJ5C0he0n8+rSYKSkoKWXSjqXNAhhC2BSvYsVqQF1S/pA2cV3AwrN9iZpdM7ghTWS/CvwE9szAWy/XuS8IYQQGuvLYKS2HwWuJA1YuAk4ut4IOChWA/oi8D+530fADODIAscB/JA0i2qtCey2BJD0J2AgcIrtmwqeO4QQQh09iYTQ02Cked8ZwBlFr1VkFNxDwA6lme0qhuDVJGl/4PU8MdGEOtffAphAai/8o6Rtbf+94lxHAUcBjBkzpsjlQwgh0E9jwUk63PYlko6v2A6A7XMbnHs3YKKkfYFhwAhJl9g+vCzNi8B9theSxo0/QSqQHig/Ue4ouwBg/PjxxaY5DSGE1Vx/DsVTmqtgeJVl7UYntn2S7dG2x5I6sm6vKHwArgX2AJA0itQkV3cGvRBCCMX02+kYbJ+ff73N9p/K9+WBCD0i6VRgsu3rSB1de0p6jBTo9KsRZy6EEHpPswWPpL2B80j98hfaPqti//HA54BFwBvAZ2z/Le9bDEzLSZ+3PZE6igxC+E/g3QW21WT7TuDO/Ht5Z5ZJoyiOr3pgCCGEHmtRLLg/A+Ntd0n6InA2cEje1217x6LXq9cHtAuwK7BBRT/QCFLJGEIIocO1IBbcHWXp7wMqu1YKq9cHNITU1zOI5ft/ZgMH9/SCIYQQ+kYP+oCqxYKrF8/ts8Dvy9aH5RA/90k6sNZBJfX6gO4C7pJ0cal9L4QQQv9RZRTcKEmTy9YvqAjHUziem6TDSbMkfKhs8xjbL0saB9wuaZrtp2vlr0gfUFeeD2gb0nDqlCM7pmMIIYQOV1HzmW57fK20FIznJukjwDeAD+XpFwCw/XL++YykO4GdgJoFUJFQPL8CHgc2A74DPEfFezohhBA6Tw+a4JbGgpM0hPQKzXXlCSTtBJwPTCwPnyZpPUlD8++jSO+Clg9eWEGRAmik7f8CFtq+y/ZngPcXuZMQQgjt06JYcOeQxgdclaflLhVQWwGTJU0F7gDOqhg9t4IiTXAL889XJO1Hqo6NLnBcCCGENmtBLLiP1DjuHmC7Zq5VpAA6PQci/XfS+z8jgC83c5EQQgh9r9ND8RQpgKbangXMYlnYnI1amqsQQggrrSfRsPtSkT6gZyVdJmnNsm031kwdQgihY3RyLLgiBdA04I+kqRI2z9uqjRUPIYTQQTo9GGmRAsi2fwp8Cbhe0j9S48WkEEIInaXZAkjS3pKekPSUpBOr7D9e0mOSHpb0f5I2Ldt3hKQn83JEo2sVKYAEkCNi/wPwVeBdhe4khBBC2zRbAyoLRroPsDXwSUlbVyQrBSPdHriaFIwUSesDJwPvI8WUO1nSevWuV6QA2rfsZl4BPgzsXeC4EEIIbVQaBVdaClgajNT2AqAUjLT8nHfY7sqr97HstZy9gFttz7A9E7iVBmVFwxlRSSVgtSR/KHI3IYQQ2qfJvp9qwUjfVyd9eTDSZgOZ1h2GXT4jagghhH6myjDsVgYjLXxsSd0ZUXN74GzbP6h3khBCCJ2nSgHUymCkLwITKo69s17+6vYB2V4M1J1SNYQQQufqq2CkpPhxe+agpOsBe+ZtNRWJhHCPpB8DVwBzSxttTylyNyGEENqj2UgIthdJKgUjHQhcVApGCky2fR3LByMFeN72RNszJJ3GstkSTrU9o971ihRAu5ZOVp5P0mi4EEIIHaonseB6Gow077sIuKjotRoWQLb3KHqyEEIInaUTIyCUFKkBkadhqJwR9dTaR4QQQmi3Tg9G2rAAkvRzYE1SJOwLgYOBSS3OVwghhJXU6QVQkUgIu9r+F2Cm7e8Au7D8ML0QQgjAggULWLBgQbuzsZz+Hoy0O//skvR20gypm7UuSyGE0H8sWLCAyy67jD322INtttmGbbbZhgkTJvCrX/2K+fPnNz5BC/UgFE+RYKS7S5oiaZGkgyv2Lc7TdJdP1V1TkQLoBknrkobeTQGeI8UHKkTSQEl/lnRDnTQHS7Kkei9IhRBCR+nu7ubQQw/ljDPO4Pnnn2fJkiUsWbKEF154gTPPPJODDz6YOXPmtC1/LQpG+jxwJHBplVN0294xLw3fIW1YANk+zfbfbf8a2BR4l+1vNTquzHHAX2rtlDScNNXD/U2cM4QQ2u7EE0/k8ccfp7u7e4V93d3dPP3003zlK19pQ86WabIJrkgw0udsPwwsWdm81QtG+rE6+7D9m0YnlzQa2A84Azi+RrLTSOG82/uUQgihCdOnT+eWW26p2+czf/587rrrLl555RU23njjPsxd0oNBCM0GI600LMeaWwScZfvaeonrjYL7xzr7DDQsgIAfAidQI6BpDumwie0bJEUBFELoN2655RYGDGjciyGJm266iU9/+tN9kKsVtSoYaQ1jbL8saRxwu6Rptp+ulbheMNKV+mtJ2h943faDkiZU2T8A+AGpLbHRuY4CjgIYM2bMymQrhBB6xezZs1m4cGHDdPPnz2fWrFl9kKMVtSoYaZ3rvZx/PiPpTmAnoPkCqETSt6ttL/Ai6m7AREn7kl5gHSHpEtuH5/3DgW2BO3M8oY2A6yRNtF1eQpNL6AsAxo8fH9OBhxDabuTIkQwZMqRq/0+5YcOGMWrUqD7K1fJ6EIpnaTBS4CVSMNLDihyYA5B22Z4vaRSpDDi73jFFRsHNLVsWk0ZHjG10kO2TbI+2PZZ0E7eXFT7YnmV7lO2xOc19pOiqk6ufMYQQOsdee+3FkiWN++Fts88++/RBjqprZhCC7UVAKRjpX4ArS8FIJU0EkPQeSS8CHwfOl/RoPnwrYLKkqcAdpD6gx+pdr0gsuO+Xr0v6DyrCczejIqpqCCH0SyNGjODjH/84V199NfPmzauaZtiwYey///6MHDmyj3OX9CQSQoFgpA+wbBru8jT3ANs1c61CseAqrAmMa+YA23eSJyYqv5GKNBN6kJcQQmibb37zm7zyyivce++9dHV1LbdvzTXXZOedd+a0005rU+46PxRPkT6gaSwbBTEQ2IDlp2YIIYTV0uDBg/n5z3/OXXfdxQUXXMAjjzwCwFZbbcVRRx3Fhz/84UIj5VqpXxdAwP5lvy8CXsvthCGEsNobMGAAe+yxB3vs0Xkz1/RkPqC+VKQAeqtifYSkt2w3Hn8YQgihbTq9Ca5I3XAK8AbwV+DJ/PuzORjdzq3MXAghhJXTbDTslQxGeoSkJ/NyRKNrFSmAbgL2zUOmR5KGYV8J/Bvw00J3FEIIoc/1ZTBSSesDJ5NC97wXODm/G1RTkQJovO2bSyu2bwF2t30fMLTA8SGEENqkD4OR7gXcanuG7ZnArcDe9S5WpA9ohqSvsWwKhkOAmbmkXOloqCGEEFqjSh9Qo1hwKxOMtNqx76h3QJEC6DBStaoU1fTuvG0g8ImCGQshhNDHqoyCaxQLbmWCkTZ9bJFICNOBYyWtbbtyZqWnCmYshBBCGzQ5Cm5lgpG+CEyoOPbOegc07AOStKukx4DH8voOkmLwQQghdLhmByFQFoxU0hBSHM+iYdNuBvaUtF4efLBn3laT7Pq1K0n3AwcD19neKW97xPa2BTPVqyS9AfxtJU4xCpjeS9npBHE/nS3up7N14v1sanuD3jiRpJtI91gy3XbdgQF5BoMfkrpZLrJ9RnkMT0nvAa4B1gPmAa/a3iYf+xng6/lUZ9j+77rXKlIA2X6fpD+XFUBTbe9Q98AOJWlygzbQfiXup7PF/XS2Ve1++psigxBekLQr4Fwl+xIpTHcIIYTQY0XeA/oCcDRpON2LwI55PYQQQuixujWg/K7Pp2z/cx/lpy9c0DhJvxL309nifjrbqnY//UqRPqA7Y66eEEIIva1IAXQGsA5wBWlabgBsT2lt1kIIIazKivQB7QpsQ5qE7vt5+Y9WZqo3SbpC0kN5eU7SQzXS1Y0A20kkHZvz+qiks2ukeU7StHzfk6ul6RQF76dfPB9Jp0h6qezf3L410vWL59PE/fSL51Mi6SuSLGlUjf2Ly+656HswoUlFIiF03ixLTbB9SOl3Sd8HZlWmKYsA+1HSQIsHJF1n+7E+y2hBkvYgBQfc3vZ8SRvWSb5HjmTRsYrcT396PtkPbBf5ktbxzyerez/97flI2oSU1+frJOu2vWMfZWm11d65YvuQJJFi111WZXfDCLAd5IvAWbbnA9h+vc35WVlF7qc/PZ/VUX97Pj8ATqB4jLPQIqtNAQR8kDSd+JNV9jUdxbWNtgQ+KOl+SXflt5KrMXCLpAclHdWH+WtWkfvpT88H4BhJD0u6qM58KP3l+UDj++k3z0fSROAl21MbJB0mabKk+yQd2Bd5Wx0VeRG140m6Ddioyq5v2P5t/v2TVK/9wMpFgO119e6H9MzWA94PvAe4UtI4rziaZDfbL+cmrVslPW77Dy3NeA29cD/96fn8DDiNlL/TSH2mn6mStr88nyL305+ez9dJMcoaGZOfzzjgdknTbD/dm/kMBQogSR+rsnkWMK1Tmn9sf6TefkmDgI8BtaYQX5kIsL2u3v1I+iLwm/wBPUnSElKspzcqzvFy/vm6pGtIzSRt+YDrhfvpN8+nnKRfADfUOEe/eD7l6txPv3g+krYDNgOmphZ5RgNTJL3X9qsV5yg9n2ck3QnsBEQB1MuKNMF9FrgQ+Oe8/AI4HviTpE+1MG+96SPA47ZfrLF/ZSLA9rVrgQ8DSNoSGEJFMEVJa0kaXvqd9I3vkT7OZ1EN74d+9HwkbVy2+k9U+bv3p+dT5H7oJ8/H9jTbG9oea3ssqeB8d2XhoxTNeWj+fRSwG3k2gNC7ihRAS4CtbB9k+yDSPOHzSbPkfa2VmetFh1LR/Cbp7ZJuBLC9CDiGFDr8L8CVtkhZC80AAATLSURBVB/t81wWcxEwTtIjpM7eI2y7/H6AtwF3S5oKTAJ+Z/umNuW3kYb308+ez9l5ePXDwB7Al2H5f2/0r+fT8H762fOpStJ4SRfm1a2Ayfn53EEaJBMFUAsUeRF1mu3tytZFan7bVmURskMIIYRmFBmE8EdJNwBX5fWDgT/kpoO/tyxnIYQQVmlFakAideB/gDTa5W7g11VGXYUQQgiFFYmEYEl3AwtIQysnReETQghhZTUchCDpE6SO0oNJkQTul3RwqzMWQghh1VakCW4q8NHSOz+SNgBu669TcocQQugMRYZhD6h44fTNgseF0JCkOb10nov7omYu6Z5WX6PieutK+re+vGYIfaVIQXKTpJslHSnpSOB3wI0NjgmhX8pRM2qyvWsfX3NdIAqgsEoqMgjhq5IOIr0NLOAC29e0PGdhtZJHW54N7EMa7HK67SskDQB+DHwIeJb0peki21fXOdfOwLnA2qSoCkfafkXSvwJHkaItPEWabr5L0sXADFK4lSmS3gLGAOPyzx/a/lE+9xzba0uaAJySz78t8CBweB60s2++/nRgCjDO9v4VeTwS2A8YBqyVg2T+lhQXbzDwzRzH8Cxgc6V5rG7N/x+/SuqPHQpcY/vk5v7aIXQI27HE0rYFmJN/HgTcCgwkRQp4HtiYNPjlRlLBsxEwEzi4ynkuzmkHA/cAG+Tth5AKLICRZelPB44tO/YGYGBePyWfYygpLt2bwOCK/E4gxUQcnfN2L+lVhWGkyNCb5XSXATdUye+RpFAw6+f1Qfz/7d27axRRGMbh35uQbpMIGixSmEZEtLMKhqAIqQWLFAqC4L9gZ6VoqQgWWtlIiiAWKiopRFFEEWLQgBfwUiUGtXFFosTP4pzAZplN3HiZMb4PDMzMmcuB3Z1vz8yZ70BPnt9ACpACBoCnDfuNAOdzWUeu93DZn6MnT6uZWraA8r/Aoh4KSnErelrta7YKQ8BYRCwA7yTdJmXHHgLGI+I7MCvp1grH2UJqkUzkhJOdwEwu2y7pOOm2Vo2UOmbReD73omuRxiialzRHCorNuQQfRs4vmFsoA0AdeBURr/M2Y6RWV5GJiPiY5wWckDRMSn/Vn8/ZbCRPk3m5BmympESmZr+iZQCKiO6/WRH77xWl9F9u/XLHmY6IwYKyC8DeiJjKt8B2NZR9btp2vmF+geLfStE27dS38Zz7gT5gR0R8k/SG1JpqJuBkRJxr4zxmleTebFYVd4BRSZ25q/8w6f2zu8A+SR2SNrI0aBR5DvRJGgSQ1CVpWy7rBmYkdZEu+H/CM1Jy1YG8PNp60yV6gbkcfHYDm/L6T6R6L7oJHJJUA5DUr+WHZTerrDUxIJ2tCZeBQWCKdOv3SETMSroE7CENA/ACeEB69lIoIr7m7thnJPWSvuOngWngaN7/LfCEpRf23yIivuRu0zckvScF0Z9xEbgi6RHwmBTIiIgPku7lbOHXI3VC2Arcz7cY68ABoBJjc5m1Y8UXUc3KJqkWEXVJ60kX9J3RNIZLlTTUV8BZ4GVEnCq7XmZV4xaQ/QuuSlpH6j59rMrBJzss6SCpvpOAn9eYFXALyMzMSuFOCGZmVgoHIDMzK4UDkJmZlcIByMzMSuEAZGZmpXAAMjOzUvwAsdlMTRuq3rwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27f181c7470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0)   # set default size of plots\n",
    "plt.subplots_adjust(wspace =20, hspace =0.7)  #调整子图间距\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]   # lr\n",
    "y_scatter = [math.log10(x[1]) for x in results]   # reg\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]         # training_acc\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy                       # validation_acc\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.370000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3072, 10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27f19b530f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "w = best_svm.W[:-1,:] # strip out the bias [D+1,C] D维度包含了bias\n",
    "print(w.shape)\n",
    "w = w.reshape(32, 32, 3, 10)    # 注意reshape的顺序，按最后一列来reshape\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min) # scale to [0,1]\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])\n",
    "\n",
    "# w = best_svm.W[:-1,:]\n",
    "# for i in range(10):\n",
    "#     plt.subplot(2, 5, i + 1)\n",
    "#     img = w[:,i].reshape(32,32,3)    # 取权重的第i列，然后reshape\n",
    "#     img = 255.0*(img-w_min)/(w_max - w_min)\n",
    "#     plt.imshow(img.astype('uint8'))\n",
    "#     plt.axis('off')\n",
    "#     plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** \n",
    "\n",
    "相当于模板匹配，每一类都会学习一个模板，对应于权重矩阵的一行或一列(与矩阵维度顺序有关)，当y=Wx+b预测的过程可以看作是用各个类别的模板来匹配测试的图像，匹配度高的判断为该模板对应的类别号，所以权重在可视化后，每个类的可视化权重图像是该类下相应特征的集合"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
